Wireless cellular deployments often are deployed with clusters of bordering cells in an extended metro or regional coverage area. The ability to support ubiquitous service coverage provided by contiguous fixed arrays of cells over populated areas is an important aspect of some mobile wireless services.
As mobile wireless users move about they tend to follow paths constrained by transportation infrastructure such as roads, paths, highways, rail lines, bridges, ferry routes, etc. These route constraints translate to handover sequences between adjoining cells that form predominant paths across sections of a regional network. It can also be understood that motion across cells containing major transportation arterials is highly correlated and predictable over periods of time that can last from minutes to hours or even days.
Another intrinsic consequence of dividing a wireless coverage network into a grid of cells with mobile users is a distribution in network load presented by varying density of users between the cells. Some cells will be lightly loaded while others are more heavily loaded. These patterns tend to shift during the day, often in a complex fashion. At times, there may be enough activity within a cell to approach or exceed resource capacity, leading to undesirable overload scenarios and poor user experience.
Because the mobility of users is complex, it is usually treated as quasi-random motion and cell resource overload is managed reactively based on refinements to initial planning, customer complaints and periodic drive testing. Autonomous methods to proactively predict and prevent resource overload would be useful to reduce operator costs and improve user satisfaction.